Magnetoencephalography (MEG) is a noninvasive technique that measures the direct consequence of neuronal activity in the living brain. MEG measures magnetic fields that emanate from the head, unimpeded by anatomy, and by the electromagnetic “right-hand rule” are associated with electrical currents generated by the flow of ions in and around active neurons. Other functional brain imaging modalities such as fMRI, PET, and optical techniques infer brain function indirectly by measuring changes in blood flow, volume, oxygenation, etc. that are hypothesized to be associated with neuronal activity. MEG is further unique in that it, together with EEG (electroencephalography), is the only noninvasive method of measuring brain function at sub-millisecond temporal resolution or better. The physiological processes underlying the indirect measures of brain function inherently evolve at a far slower timescale.
Cohen primary reported detecting a magnetic signal originating from the human brain in 1968 using a non-superconducting sensor [1]. Shortly thereafter a RF (radio frequency) SQUID (Superconducting QUantum Interference Device) sensor was used for the first time to measure biomagnetic signals originating from the human heart [2], and the human brain [3]. The evoked-response brain activity measured with a SQUID sensor was reported by Brenner, et al., in 1975 [4]. Once it was clear that human brain activity could be observed and measured non-invasively by instruments consisting of one or a few sensors, the goal quickly shifted to precisely localizing the neuronal sources responsible for the measured magnetic activity. The position and vector characteristics of these neuronal sources can be estimated from the inverse solution of the field distribution measured outside the head.
Magnetic field distributions were originally obtained using single sensor systems that mapped fields around the head by moving the sensor in space and presenting the same stimulus to the subject at each sensor location. By the mid-1990's multiple sensors arrays covering much of the head were commercially available from companies including 4D-Neuroimaging in the US, CTF in Canada, and Neuromag in Finland, with large projects also in place in Japan. These systems consisted of 100 sensors or more spaced at various intervals over the head using various configurations. Sensor arrangement variables including gradiometric configurations, sensor spacing and other parameters have been quantitatively modeled by Flynn [5] and Mosher [6] for performance optimization. Different designs are driven by various design parameters impacting detection efficiency, cost, and target application.
Although MEG is not truly tomographic, and source reconstruction is limited by solutions of the electromagnetic inverse problem, constraints used for source localization have demonstrated reliable and accurate results. Current MEG instrumental source location accuracy reported in the literature is approximately 2 to 4 mm, depending on signal-to-noise (S/N) of the measured MEG signal, source parameters, and a variety of other parameters. Typically, 5 to 10 mm accuracy is attained in most medical applications with the latest instruments. In addition to locating the sources of neuronal activity, MEG temporal resolution is unsurpassed by any other brain imaging method.
A typical MEG experiment involves numerous successive presentations of a given stimulus or group of stimuli to a subject for which the evoked brain response is measured. An exemplary protocol would be stimulating the receptor nerves on a fingertip using a pneumatic button. The activated touch receptors transmit a signal via peripheral afferents and the spinal cord to the thalamus, which in turn transmits the signal via third-order fibers to activate a small portion of the sensory region of cerebral cortex (primary somatosensory cortex). Neurons in the cortex are arranged in columnar structures that are associated with the receptive field location and the type of stimulus.
As neurons are activated, positive ions are rapidly transported through the cell membrane into the cell (depolarization) followed by a slightly slower outflow of positive ions (repolarization). The entire process lasts approximately 3 milliseconds (msec), not including the afferent signal propagation. This phenomenon propagates along some portion of a neuron generating a concentrated ionic flow or intracellular current inside the neuron.
MEG measures the magnetic fields that are associated through Maxwell's equations with movement of charge. Although electrodes implanted in cortex near or inside of cell bodies can measure the response of individual neurons, it is impossible for current technology to observe such a signal noninvasively (i.e., from outside the skull). MEG is a viable technique because all of the neurons in a given cortical column, or set of columns, respond roughly synchronously to a single stimulus, resulting in a superposition of the fields from hundreds or thousands of activated neurons. Nonetheless, the typical MEG signal measured during evoked response experiments is very tiny, only tens to hundreds of femtotesla (10−14 to 10−13T). MEG instruments today universally use SQUID sensors to measure these extraordinarily small magnetic fields. A state-of-the-art production SQUID will have sensitivities down to ˜2 fT/√Hz (one femtotesla, fT, is 10−15T).
The greatest challenge to measuring magnetic fields resulting from brain activity and localizing neuronal sources is the massive amount of magnetic noise in our environment. Magnetic noise in an urban setting is commonly in the range of 10−9 to 10−5T generated by AC line noise, automobiles, trains, elevators, etc. Urban magnetic noise is many orders of magnitude larger than the fields produced by the brain. A variety of techniques have been used to reduce the ambient magnetic fields for MEG measurements including: 1) locating the MEG instrument and subject in a magnetically shielded room (MSR), 2) using gradiometric magnetic field sensors coupled to SQUIDs, 3) active compensation of magnetic noise with field coils, 4) measuring or estimating the ambient noise fields and digitally subtracting the noise from sensors that are measuring brain activity, and 5) averaging MEG data from numerous stimuli. In addition to these hardware-based approaches, a broad array of additional post-processing software algorithms have been developed to reduce noise in MEG data, such as simple filters (low-pass, high-pass, band-stop, etc.), ICA [7, 8], etc.
A typical two-layer MSR will reduce noise by at least 30 dB at 0.1 Hz and 80 dB at 100 Hz for a cost of several hundred thousand US dollars. Gradiometers will further reduce noise at the cost of some reduction of brain signal. Gradiometer performance depends on numerous parameters including gradiometer design (e.g., gradiometer order and baseline), quality of fabrication, and characteristics of the noise source (e.g., physical size, proximity). Typical primary-order gradiometers reduce ambient magnetic fields from sources such as AC power lines by about 40 dB.
Active noise compensation involves measuring the noise field and using sets of large magnetic field coils relatively distant from the MEG system to cancel the noise field by superposition [9]. Such systems produce a limited set of spatial and temporal frequencies (due to the magnetic field coil size and inductance), are extremely sensitive to mechanical effects such as vibration, and are costly. A second form of active field cancellation uses the modulation and feedback coil common to most SQUID sensors to generate the noise compensation field at the SQUID pickup loop [10]. This method has greater frequency response (though still limited by processing and conversion speeds), fewer mechanical issues, and is less costly. This approach enables suppressing ambient fields with higher spatial frequencies because the background fields can be measured in close proximity to the MEG sensors and the field compensation is performed directly at the MEG sensors. The drawback is that the ambient field sensors placed close to the MEG array will be sensitive to sources in the brain; consequently, some brain signal will be cancelled along with the background. Although active noise cancellation techniques have the advantage of reducing the dynamic range of the MEG signals (typically dominated by ambient noise), any noise inherent in the background field measurement and generation of cancellation fields will be irreversibly added to the MEG signals.
Reference sensors are used by most modern MEG systems to further reduce background by measuring the ambient fields near the MEG sensor array, projecting the ambient field to the MEG sensors, and digitally subtracting the ambient field contribution to the signal measured by the MEG sensors [11, 12]. Large dynamic range analog and digital electronics are required for both background and MEG signals to avoid saturation. Such large dynamic range electronics have only become commercially available and affordable in recent years. Additionally, reference sensors must measure the background field with low noise and sufficient resolution to minimize increasing the noise floor of the MEG signals.
Simultaneous sampling of ambient and MEG fields with subsequent projection and subtraction of background from brain signals effectively removes noise up to the Nyquist limit and eliminates phase lag caused by real-time conversion, processing, and feedback inherent in all active noise cancellation approaches. In order to accurately characterize the ambient field at the primary MEG sensors, the reference sensors must be placed as close as possible to the MEG array. However, as the reference sensors approach the MEG array, they will be increasingly sensitive to magnetic fields originating in the brain. Any method that projects fields measured at the reference sensors to the primary MEG sensors and subtracts the projected values will necessarily subtract some portion of the brain signal contained in the reference sensor measurement. Consequently, there is a tradeoff between placing reference sensors as close to the MEG sensors as possible (to accurately characterize the spatial frequency of ambient fields) while minimizing the brain signal contained in the reference sensor measurement.
Other techniques have been developed using reference sensors in close proximity to the primary MEG sensors or using the MEG sensors themselves to measure the ambient background fields. These techniques invariably apply a. model-based description of the noise to separate the ambient field contribution from the fields originating in the brain. Although this approach has been shown to be quite effective, it suffers from the imperfect model describing the noise and consequently does not completely remove ambient field noise from the MEG signal. It may also remove some signal originating from the brain that is not entirely orthogonal to the model description of the ambient noise sources.
Finally, averaging MEG data from multiple stimuli (data epochs) is commonly used to increase MEG signal-to-noise (S/N). Every presentation of the stimulus evokes a reproducible and nearly identical cortical response; consequently the S/N of evoked response measurements can be greatly improved by averaging the data from a large number of stimuli. In theory, this averaging reduces both ambient field noise and brain activity that is uncorrelated with the stimuli. Although the noise is reduced by roughly 1/√Ne (where Ne is the number of epochs recorded and averaged), the evoked response signal begins to degrade for large numbers of stimuli. Consequently S/N does not increase indefinitely. Furthermore, averaging epochs is not possible for all functional brain studies (e.g. epilepsy) and there is increasing evidence that effects such as alterations in the dynamics of ongoing neural synchrony would be washed out by averaging multiple epochs of evoked response recordings.
Various objects, advantages and novel features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.